RSNA 2017: Rads who use AI will replace rads who don’t

Artificial intelligence (AI) is here to stay in radiology—and so are radiologists.

Curtis Langlotz, MD, PhD, of Stanford made the case during a how-to session Nov. 27 at the annual meeting of the Radiological Society of North America in Chicago.

Langlotz launched into this part of his presentation by quoting the psychologist, computer scientist and AI pioneer Geoffrey Hinton, PhD, who told a New Yorker writer in 2016: “[I]f you work as a radiologist you are like Wile E. Coyote in the cartoon. You’re already over the edge of the cliff, but you haven’t yet looked down. It’s just completely obvious that in five years deep learning is going to do better than radiologists.”

Multiple outlets soon piled on, publishing pieces that predicted the demise of radiologists’ livelihood as an inevitable if not yet imminent development to watch for.

Langlotz rattled off several headlines and quotes that attained instant infamy within radiology, then proceeded to make a case as to why such prognostications are based more on fantasy than fact.

“How many of you would like to fly in an airplane with no human pilot?” he asked before delving deeper into the analogy. Flying an airplane is a very similar activity to interpreting radiology exams, he suggested, as it’s a perceptual activity, it’s high-risk, and it requires the integration and digestion of loads of data before a decision can be made on what to do next.

It’s good that today’s airline pilots have autopilot capabilities to take care of the monotonous, repetitive and data-crunching parts of flying that most pilots are only to happy to delegate to a computer, Langlotz said. At the same time, he added, most people are happy to have a human in the cockpit to take the controls as needed and when an autopilot shouldn’t be flying a plane solo.

“I think that’s going to be our role” as radiologists, he said. “We’re going to get better at getting our ‘autopilot’ to augment what we do and to make our lives better and easier.”

Technology drives radiology—and vice versa

Langlotz supported his case with recently published research showing that radiology ranks above average, relative to other specialties, when it comes to gauging levels of clinical complexity regularly dealt with. The implication was that the argument singling out radiology as the specialty most ripe for displacement by deep learning is built on shaky ground.

He also displayed a graph showing how wrong were the prognosticators who warned young people to avoid entering work as bank tellers when ATMs began rolling out en masse. The graph showed teller headcounts rising along with ATM installations at nearly parallel rates.

“ATMs expanded the services banks can provide but didn’t put tellers out of work,” Langlotz underscored.

He then dipped into the history books and showed a photo of William Morton, who was instrumental in popularizing the use of x-rays in the U.S. in the 1890s. Sitting having his hand x-rayed by Morton in the photo is the engineer Edwin Hammer.

Back then, radiologists—who were not yet called radiologists—naturally partnered with engineers, Langlotz noted.

Radiology was a high-tech field from its beginning, he added, “and we have always been a high-tech specialty. We have partnered with [technology innovators] and learned how to adopt new technologies.”

Future bright for AI adopters

“When MRI came in, people said, ‘Wow. The images are so clear, we’re not going to need radiologists,’” Langlotz said. And what happened? “We have learned the physics of MR. We understand the artifacts. We know when those MR images are telling us something that’s real versus an artifactual [effect].

“We know that image, we own that learning, and I think the same is going to be true for us with deep learning. We’re going to learn how to deploy it clinically, when it’s worth using and when it shouldn’t be used.”